Markets Quantitative Analyst - Capital Analytics

Citi•New York, NY

2d•$150,000 - $175,000•Hybrid

About The Position

Want to build quantitative models that directly influence trading decisions and optimise how capital is deployed across global markets? Citi's Capital Analytics team sits at the intersection of quantitative modelling, technology, and front-office trading. We develop the analytics and systems that help traders understand the capital impact of their activities in real time, enabling smarter pricing, more efficient balance sheet usage, and better risk-adjusted returns. This is a unique opportunity to work on large-scale quantitative challenges, applying advanced mathematics, programming, and financial modelling to problems that have a direct impact on business performance across Citi's global markets franchise.

Requirements

Strong quantitative background (Master's/PhD) in Mathematics, Physics, Engineering, Computer Science, Quantitative Finance, or a related discipline
Experience developing quantitative models and analytics within a financial markets environment
Strong Python and/or C++ programming skills
Solid understanding of probability, statistics, numerical methods, and financial modelling
Excellent problem-solving and communication skills, with the ability to explain quantitative concepts to both technical and business stakeholders
Interest in working close to trading desks and applying quantitative techniques to real-world business challenges

Responsibilities

Develop and enhance cross-asset quantitative models used to measure and optimise capital across global trading businesses
Build analytical frameworks supporting pricing, balance sheet optimisation, and capital-efficient trade structuring
Partner closely with traders, structurers, and quantitative analysts to deliver actionable insights that influence front-office decision-making
Design and implement scalable Python and C++ analytics used across trading, risk, and capital management platforms
Work on large datasets and complex portfolios to improve modelling accuracy, performance, and efficiency
Contribute throughout the full model lifecycle, from research and development through to implementation and production support